Introduction to smart antenna

1. Introduction to Smart Antennas

2. Copyright 2007 by Morgan & Claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any meanselectronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Introduction to Smart Antennas Constantine A. Balanis, Panayiotis I. Ioannides www.morganclaypool.com ISBN: 1598291769 paperback ISBN: 9781598291766 paperback ISBN: 1598291777 ebook ISBN: 9781598291773 ebook DOI: 10.2200/S00079ED1V01Y200612ANT005 A Publication in the Morgan & Claypool Publishers series SYNTHESIS LECTURES ON ANTENNAS #5 Lecture #5 Series Editor: Constantine A. Balanis, Arizona State University First Edition 10 9 8 7 6 5 4 3 2 1

3. Introduction to Smart Antennas Constantine A. Balanis Panayiotis I. Ioannides Department of Electrical Engineering Arizona State University SYNTHESIS LECTURES ON ANTENNAS #5 M&C M o r g a n &C l a y p o o l P u b l i s h e r s

4. iv ABSTRACT As the growing demand for mobile communications is constantly increasing, the need for better coverage, improved capacity and higher transmission quality rises. Thus, a more efcient use of the radio spectrum is required. Smart antenna systems are capable of efciently utilizing the radio spectrum and, thus, is a promise for an effective solution to the present wireless systems problems while achieving reliable and robust high-speed high-data-rate transmission. The purpose of this book is to provide the reader a broad view of the system aspects of smart antennas. In fact, smart antenna systems comprise several critical areas such as individual antenna array design, signal processing algorithms, space-time processing, wireless channel modeling and coding, and network performance. In this book we include an overview of smart antenna concepts, introduce some of the areas that impact smart antennas, and examine the inuence of interaction and integration of these areas to Mobile Ad-Hoc Networks. In addition, the general principles and major benets of using spacetime processing are introduced, especially employing multiple-input multiple-output (MIMO) techniques. KEYWORDS Adaptive arrays, Switched-beam antennas, Phased array, SDMA, Mutual coupling, Direction of arrival, Adaptive beamforming, Channel coding, MANET, Network throughput, Space time processing.

5. v Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Mobile Communications Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 2.1 General Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Cellular Communications Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 2.3 The Evolution of Mobile Telephone Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 The Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.5 Cellular Radio Systems: Concepts and Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.5.1 Omnidirectional Systems and Channel Reuse. . . . . . . . . . . . . . . . . . . . . . .10 2.5.2 Cell Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.5.3 Sectorized Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.6 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.6.1 Spectral Efciency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.7 Multiple Access Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.7.1 FDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.7.2 TDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.7.3 CDMA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 2.7.4 OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3. Antenna Arrays and Diversity Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1 Antenna Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Antenna Classication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.1 Isotropic Radiators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22 3.2.2 Omnidirectional Antennas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22 3.2.3 Directional Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.4 Phased Array Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.5 Adaptive Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 Diversity Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4. Smart Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Need for Smart Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

6. vi INTRODUCTION TO SMART ANTENNAS 4.4 Smart Antenna Congurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.4.1 Switched-Beam Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.4.2 Adaptive Antenna Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42 4.5 Space Division Multiple Access (SDMA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45 4.6 Architecture of a Smart Antenna System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.6.1 Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.6.2 Transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.7 Benets and Drawbacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53 4.8 Basic Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.9 Mutual Coupling Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .64 5. DOA Estimation Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.2 The Array Response Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.3 Received Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.4 The Subspace-Based Data Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75 5.5 Signal Autocovariance Matrices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77 5.6 Conventional DOA Estimation Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78 5.6.1 Conventional Beamforming Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.6.2 Capons Minimum Variance Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.7 Subspace Approach to DOA Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.7.1 The MUSIC Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.7.2 The ESPRIT Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.8 Uniqueness of DOA Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6. Beamforming Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.1 The Classical Beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.2 Statistically Optimum Beamforming Weight Vectors . . . . . . . . . . . . . . . . . . . . . . . 89 6.2.1 The Maximum SNR Beamformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 6.2.2 The Multiple Sidelobe Canceller and the Maximum SINR Beamformer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91 6.2.3 Minimum Mean Square Error (MMSE) . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.2.4 Direct Matrix Inversion (DMI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 6.2.5 Linearly Constrained Minimum Variance (LCMV) . . . . . . . . . . . . . . . . . 95 6.3 Adaptive Algorithms for Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6.3.1 The Least Mean-Square (LMS) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 97 6.3.2 The Recursive Least-Squares (RLS) Algorithm . . . . . . . . . . . . . . . . . . . . . 99

7. CONTENTS vii 6.3.3 The Constant-Modulus (CM) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.3.4 The Afne-Projection (AP) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.3.5 The Quasi-Newton (QN) Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 7. Integration and Simulation of Smart Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 7.1 Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 7.2 Antenna Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 7.3 Mutual Coupling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 7.4 Adaptive Signal Processing Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111 7.4.1 DOA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111 7.4.2 Adaptive Beamforming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .112 7.4.3 Beamforming and Diversity Combining for Rayleigh-Fading Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 7.5 Trellis-Coded Modulation (TCM) for Adaptive Arrays. . . . . . . . . . . . . . . . . . . .114 7.6 Smart Antenna Systems for Mobile Ad Hoc NETworks (MANETs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 7.6.1 The Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 7.6.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 7.7 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .122 8. SpaceTime Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .125 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 8.2 Discrete SpaceTime Channel and Signal Models. . . . . . . . . . . . . . . . . . . . . . . . .129 8.3 SpaceTime Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 8.4 Intersymbol and Co-Channel Suppression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135 8.4.1 ISI Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 8.4.2 CCI Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 8.4.3 Joint ISI and CCI Suppression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .137 8.5 SpaceTime Processing for DS-CDMA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .137 8.6 Capacity and Data Rates in MIMO Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138 8.6.1 Single-User Data Ratec Limits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .139 8.6.2 Multiple-Users Data Rate Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 8.6.3 Data Rate Limits Within a Cellular System . . . . . . . . . . . . . . . . . . . . . . . 143 8.6.4 MIMO in Wireless Local Area Networks . . . . . . . . . . . . . . . . . . . . . . . . . 145 8.7 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148 9. Commercial Availability of Smart Antennas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149

8. viii INTRODUCTION TO SMART ANTENNAS 10. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .159

9. 1 C H A P T E R 1 Introduction In recent years a substantial increase in the development of broadband wireless access technologies for evolving wireless Internet services and improved cellular systems has been observed [1]. Because of them, it is widely foreseen that in the future an enormous rise in trafc will be expe- rienced for mobile and personal communications systems [2]. This is due to both an increased number of users and introduction of new high bit rate data services. This trend is observed for second-generation systems, and it will most certainly continue for third-generation systems. The rise in trafc will put a demand on both manufacturers and operators to provide sufcient capacity in the networks [3]. This becomes a major challenging problem for the service providers to solve, since there exist certain negative factors in the radiation environment contributing to the limit in capacity [4]. A major limitation in capacity is co-channel interference caused by the increasing number of users. The other impairments contributing to the reduction of system performance and capacity are multipath fading and delay spread caused by signals being reected from structures (e.g., buildings and mountains) and users traveling on vehicles. To aggravate further the capacity problem, in 1990s the Internet gave the people the tool to get data on-demand (e.g., stock quotes, news, weather reports, e-mails, etc.) and share information in real-time. This resulted in an increase in airtime usage and in the number of subscribers, thus saturating the systems capacity. Wireless carriers have begun to explore new ways to maximize the spectral efciency of their networks and improve their return on investment [5]. Research efforts investigating methods of improving wireless systems performance are currently being conducted worldwide. The deployment of smart antennas (SAs) for wireless communications has emerged as one of the leading technologies for achieving high efciency networks that maximize capacity and improve quality and coverage [6]. Smart Antenna systems have received much attention in the last few years [611] because they can increase system capacity (very important in urban and densely populated areas) by dynamically tuning out interference while focusing on the intended user [12, 13] along with impressive advances in the eld of digital signal processing. Selected control algorithms, with predened criteria, provide adaptive arrays the unique ability to alter the radiation pattern characteristics (nulls, sidelobe level, main beam direction,

10. 2 INTRODUCTION TO SMART ANTENNAS and beamwidth). These control algorithms originate from several disciplines and target specic applications (e.g., in the eld of seismic, underwater, aerospace, and more recently cellular communications) [14]. The commercial introduction of SAs is a great promise for big increase in system performance in terms of capacity, coverage, and signal quality, all of which will ultimately lead to increased spectral efciency [14]. As the necessity of exchanging and sharing data increases, users demand ubiquitous, easy connectivity, and fast networks whether they are at work, at home, or on the move. Moreover, these users are interested in interconnecting all their personal electronic devices (PEDs) in an ad hoc fashion. This type of network is referred to as Mobile Ad hoc NETwork (MANET), and it is beginning to emerge using BluetoothTM technology. BluetoothTM is a short-range, low-power radio link (10100 m) that allows two or more BluetoothTM devices to form a communication channel and exchange data [15, 16]. Because BluetoothTM uses an omnidirectional antenna (operating in the unlicensed 2.4 GHz industrial, scientic, and medical (ISM) band), it lacks the ability to steer the radiation beam toward the intended users and form nulls to cancel jammers. This limits the overall system capacity or network throughput of MANETs. Furthermore, because of the omnidirectional antenna, battery life in PEDs is reduced since energy is radiated everywhere and not just toward the desired user. Consequently, the benets provided by smart antennas would enhance the overall performance of MANETs [17]. Current trends concentrate on spacetime processing and coding, a technique that promises to greatly improve the performance in wireless networks by using multiple antennas at the transmitter and the receiver [18]. Spacetime processing can be viewed as an evolution of the traditional array signal processing techniques such as antenna array and beamforming. Operating simultaneously on multiple sensors, spacetime receivers process signal samples both in time and space, thereby improving resolution, interference suppression, and service quality. Sophisticated spacetime processing methods applied to multiple-input multiple-output (MIMO) systems are expected to provide great capacity and data rate increases in cellular systems and wireless local area networks. This book is organized as follows: in Chapter 2 an overview of wireless communication systems is presented, a requisite to analyze smart antenna systems. Following this, a chapter on antenna arrays and diversity techniques is included that describes antenna properties and classies them according to their radiation characteristics. In Chapter 4, the functional principles of smart antennas are analyzed, different smart antenna congurations are exhibited and the benets and drawbacks concerning their commercial introduction are highlighted. Chapter 5 deals with different methods of estimating the direction of arrival. The more accurate this estimate is, the better the performance of a smart antenna system. Chapter 6 is devoted to beamforming techniques through which the desired radiation patterns of the adaptive arrays are

11. INTRODUCTION 3 achieved. The succeeding chapter presents the results of a project that examines and integrates antenna design, adaptive algorithms and network throughput. Chapter 8 is devoted to space time processing techniques. The fundamental principles are analyzed and, through experimental results, the enormous improvements in data rates and capacities realized with MIMO systems are demonstrated. Before the book is concluded, commercial efforts and products of smart antenna are briey reviewed in Chapter 9. This book is a comprehensive effort on smart antenna systems and contains material extracted from various sources. The authors have attempted to indicate, in the respective chapters of the book, the sources from which the material was primarily derived and its development based upon. In particular, the authors would like to acknowledge that major contributions were derived from many references, especially [17, 1929]. Also, the authors have contacted most of the primary authors of these references, who gracefully and promptly responded favorably. In fact, some of the authors provided expeditiously gures and data included in this book. Acknowledgement of the sources is indicated in the respective gures.

12. 5 C H A P T E R 2 Mobile Communications Overview In this chapter, a brief overview of mobile communications is presented to understand its functional principles and introduce the necessary terminology for the rest of this book. 2.1 GENERAL DESCRIPTION All communication systems have fundamentally the same goal: to pass along the maximum amount of information with the minimum number of errors [19]. Modern digital wireless communications systems are no exception. These systems can usually be separated into several elements as indicated by Fig. 2.1. Given any digital input, the source encoder eliminates redundancy in the information bits, thus maximizing the amount of the useful information transferred in the communications system [19]. The output of the source generator is processed by the channel encoder, which incorporates error control information in the data to minimize the probability of error in transmission. The output of the channel encoder is further processed by the Digital Signal Processing unit, in order to allow simultaneous communication of many users. An example of this would be digital beamforming, which by using the geometric properties of the antenna array, is able to concentrate signals from multiple users in different desired directions, allowing more users to be served by the system. The generated data stream is then processed by the modulator which is responsible to shift the baseband signal at its input into the band-pass version at the output, due to the bandwidth constraints of the communication system [19]. The information sequence generated at the output of the modulator is then fed into the antenna array and transmitted through the wireless channel. On the other end of the radio channel, the reverse procedure takes place. The demodulator down converts the signals from different users collected by the receiver antenna into their baseband equivalent. The Digital Signal Processor then separates the different signals that come from different users. The channel decoder detects and corrects, if possible, errors that are caused due to propagation through the physical channel. Following that, the source decoder restores the actual data sequence from its compressed version. The entire procedure aims to recover the information transmitted on the other end of the physical channel, with the least possible number of errors.

13. 6 INTRODUCTION TO SMART ANTENNAS Data Source Source + Channel Encoder Digital Signal Processing Digital Modulator Transmitting Antenna Physical Channel Data Sink Source + Channel Decoder Digital Signal Processing Digital Demodulator Receiving Antenna FIGURE 2.1: Elements of a communications system [19]. 2.2 CELLULAR COMMUNICATIONS OVERVIEW The wireless communications era began around 1895 when Guglielmo Marconi demonstrated the use of radio waves to communicate over large distances. Cellular is currently one of the fastest growing and most demanding telecommunications applications. Today, it represents the dominant percentage of all new telephone subscriptions around the world. During the early part of this decade, the number of mobile cellular subscribers has surpassed that of conventional xed lines [30]. In many parts of the world, cell phone penetration is already over 100% and the market is still growing. According to the latest gures from Wireless Intelligence (WI) [31], the venture between Ovum and the GSM Association that focuses on market data and analysis on the global wireless industry, worldwide growth is currently running at over 40 million new connections per monththe highest volume of growth the market has ever seen. Overall, world market penetration is expected to rise from an estimated 41% at the end of 2006 to 47% by the end of 2007, on a track to hit the landmark of 3 billion cellular connections! However, as Wireless Intelligence says, the number of cellular connections does not represent the number of cellular users, since many subscribers have more than one cellular connection and, in addition, these gures include accounts that may no longer be active. In general, subscriber growth is especially strong in Asia, where penetration rates are still low, followed by the Americas while the saturated Western European market is stagnant [32]. The charts in Fig. 2.2 graph Micrologic Researchs [33] estimates (a) of the annual worldwide cellular telephone sales and (b) worldwide number of cellular subscribers from 1998 to 2006. 2.3 THE EVOLUTION OF MOBILE TELEPHONE SYSTEMS The concept of cellular service is the use of low-power transmitters where frequencies can be reused within a geographic area. However, the Nordic countries were the rst to introduce

14. MOBILE COMMUNICATIONS OVERVIEW 7 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year 0 100 200 300 400 500 600 700 MillionsofUnits 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year 0 500 1000 1500 2000 MillionsofSubscribers FIGURE 2.2: (a) Annual worldwide cellular handset shipments and (b) worldwide number of cellular subscribers [34]. cellular services for commercial use with the introduction in 1981 of the Nordic Mobile Telephone (NMT). Cellular systems began in the United States with the release of the advanced mobile phone service (AMPS) system in 1981. The AMPS standard was adopted by Asia, Latin America, and Oceanic countries, creating the largest potential market in the world for cellular technology [35]. In the early 1980s, most mobile telephone systems were analog rather than digital, like todays newer systems. One challenge facing analog systems was the inability to handle the growing capacity needs in a cost-efcient manner. As a result, digital technology was welcomed. The advantages of digital systems over analog systems include ease of signaling, lower levels of interference, integration of transmission and switching, and increased ability to meet capacity demands [35]. GSM, which was rst introduced in 1991, is one of the leading digital cellular systems. Today, it is the de facto wireless telephone standard in Europe, and it is widely used in Europe and other parts of the world. CDMA system was rst standardized in 1993. CDMA refers to the original ITU IS- 95 (CDMA) wireless interface protocol and is considered a second-generation (2G) mobile wireless technology which was commercially introduced in 1995. It quickly became one of the worlds fastest-growing wireless technologies. In 1999, the International Telecommunications Union selected CDMA as the industry standard for new third-generation (3G) wireless systems. Many leading wireless carriers are now building or upgrading to 3G CDMA networks in order to provide more capacity for voice trafc, along with high-speed data capabilities [36]. The new version of CDMA, also known as CDMA2000 or IS-2000, is both an air interface and a core network solution for delivering

15. 8 INTRODUCTION TO SMART ANTENNAS the services that customers are demanding today [37]. A key component of CDMA2000 is its ability to support the full demands of advanced 3G services such as multimedia and other IP-based services. CDMA2000 is the ideal solution for wireless operators who want to take advantage of the new market dynamics created by mobility and the Internet [37]. Universal Mobile Telecommunications System (UMTS) is an evolution of the GSM system. The air interface has been changed from a Time Division Multiple Access (TDMA) based system to a Wideband Code Division Multiple Access (W-CDMA) based air interface. This change was needed to achieve the data rate of 2 Mbps to the mobile which is a 3G requirement [38]. Besides voice and data, UMTS will deliver audio and video to wireless devices anywhere in the world through xed, wireless, and satellite systems. The UMTS system will serve most of the European countries. Table 2.1 charts the worldwide development of Mobile Telephone Systems. 2.4 THE FRAMEWORK Wireless communication systems usually perform duplex communication between two points [1]. These two points are usually dened as the Base Station (BS) and the Mobile TABLE 2.1: The Development of Mobile Telephone Systems[35] YEAR MOBILE SYSTEM 1981 Nordic Mobile Telephone (NMT) 450 1983 American Mobile Phone System (AMPS) 1985 Total AccessCommunication System (TACS) 1986 Nordic Mobile Telephony (NMT) 900 1991 American Digital Cellular (ADC) 1991 Global System for Mobile Communication (GSM) 1992 Digital Cellular System (DCS) 1800 1993 CDMA One 1994 Personal Digital Cellular (PDC) 1995 PCS 1900-Canada 1996 PCSUnited States 2000 CDMA2000 2005 UMTS

16. MOBILE COMMUNICATIONS OVERVIEW 9 Station (MS). The data communication from the BS to the MS is usually referred to as the downlink or forward channel. Similarly, the data communication from the MS to the BS is usually referred to as the uplink or reverse channel. Two systems can exist in the downlink: an antenna system for transmission at the BS and another antenna system for reception at the MS. Additionally, there can be two systems in the uplink: transmission at the MS and reception at the BS [1]. An example of such a system is illustrated in Fig. 2.3. The cellular telephone system provides a wireless connection to the Public Switched Telephone Network (PSTN) for any user in the radio range of the system [39]. It consists of r Mobile stations r Base stations, and r Mobile Switching Center (MSC). The base station is the bridge between the mobile users and the MSC via telephone lines or microwave links [39]. The MSC connects the entire cellular system to the PSTN in the cellular system. Fig. 2.4 provides a simplied illustration how a cellular telephone system works. N K Mobile station Transmit Process Receive Process M L Transmit Process Base station Transmit Data Receive Process Wireless Channel Receive Data Transmit Data Receive Data FIGURE 2.3: A general antenna system for broadband wireless communications [1].

17. 10 INTRODUCTION TO SMART ANTENNAS Switching Center Public Switched Telephone Network Base Station Base Station Base Station Base Station Antenna Antenna Antenna Antenna Link Link Link Link FIGURE 2.4: A typical setup of a base mobile system [40]. 2.5 CELLULAR RADIO SYSTEMS: CONCEPTS AND EVOLUTION Maintaining capacity has always been a challenge as the number of services and subscribers increased. To achieve the capacity demand required by the growing number of subscribers, cellular radio systems had to evolve throughout the years. To justify the need for smart antenna systems in the current cellular system structure, a brief history in the evolution of the cellular radio systems is presented. For in-depth details, the reader is referred to [13, 40, 41]. 2.5.1 Omnidirectional Systems and Channel Reuse Since the early days, system designers knew that capacity was going to be a problem, especially when the number of channels or frequencies allocated by the Federal Communications Commission (FCC) was limited. Therefore, to accommodate the huge number of subscribers and achieve the required capacity, a suitable cellular structure had to be designed. The dominant concept is that the capacity may only be increased by using each trafc channel to carry many calls simultaneously [40]. One way to accomplish this is to use the same channel over and over. To do so, mobile phones using the same radio channel have to be placed sufciently apart from each other in order to avoid disturbance. Cellurization consists of breaking up a large geographical service area into smaller areas, referred to as cells, each of which can use a portion of the available bandwidth (frequency reuse), thus making it possible to provide wireless links to many users despite the limited spectrum [42]. Cells, usually, have irregular shapes and dimensions. The shape is determined largely by the terrain and man-made features. Depending

18. MOBILE COMMUNICATIONS OVERVIEW 11 cellR D FIGURE 2.5: Typical cellular structure with 7 cells reuse pattern. on their size, cells can be classied as macrocells (where the base station has sufcient transmit power to cover areas of radius 120 km), microcells (areas of 0.1 to 1 km in radius), and picocells (indoor environment) [42]. A minimum distance between two cells using identical channels is required, known as the channel reuse distance. This is also known as channel reuse via spatial separation [43]. The capacity of the system depends on this distance. An example of such a structure is depicted in Fig. 2.5. In Fig. 2.5, each hexagonal area with different shade represents a small geographical area named cell with maximum radius R [44]. At the center of each cell resides a base station equipped with an omnidirectional antenna with a given band of frequencies. Base stations in adjacent cells are assigned frequency bands that contain completely different frequencies than neighboring cells. By limiting the coverage area within the boundaries of a cell, the same band of frequencies may be used to cover different cells that are separated from each other by distances large enough (indicated as D in Fig. 2.5) to keep interference levels below the threshold of the others. The design process of selecting and allocating the same bands of frequencies to different cells of cellular base stations within a system is referred to as frequency reuse or channel reuse [41]. This is shown in Fig. 2.5 by repeating the shaded pattern or clusters [13]; cells having the same shaded pattern use the same frequency bandwidth. In the rst cellular radio systems deployed, each base station was equipped with an omnidirectional antenna [4]. Because only a small percentage of the total energy reached the desired user, the remaining energy was wasted and polluted the environment with interference. As the number of users increased, so did the interference, thereby reducing capacity. An immediate solution to this

19. 12 INTRODUCTION TO SMART ANTENNAS cell microcell FIGURE 2.6: Cell-splitting. problem was to subdivide a cell into smaller cells; this technique is referred to as cell splitting [44]. 2.5.2 Cell Splitting Cell-splitting [44], as shown in Fig. 2.6, subdivides a congested cell into smaller cells called microcells, each with its own base station and a corresponding reduction in antenna height and transmitter power. Cell-splitting improves capacity by decreasing the cell radius R and keeping the D/R ratio unchanged; D is the distance between the centers of the clusters. The disadvantages of cell-splitting are costs incurred from the installation of new base stations, the increase in the number of handoffs (the process of transferring communication from one base station to another base station when the mobile unit travels from one cell to another), and a higher processing load per subscriber. 2.5.3 Sectorized Systems As the demand for wireless service grew even higher, the number of frequencies assigned to a cell eventually became insufcient to support the required number of subscribers. Thus, a cellular design technique was needed to provide more frequencies per coverage area. Sectorized systems subdivide the traditional cellular area into sectors that are covered using directional antennas at the same base station, as shown in Fig. 2.7. This technique is referred to as cell-sectoring [41] where a single omnidirectional antenna is replaced at the base station with several directional antennas. Operationally, each sector is treated as a different cell in the system, the range of which, in most cases, can be greater than in the omnidirectional case (roughly 35% greater), since the transmission power is focused to a smaller area [20]. Sectorized cells can increase the efcient use of the available spectrum by reducing the interference presented by the base station and its users to the rest of the network, and they are widely used for this purpose. Most systems in commercial service today employ three sectors, each one with 120 coverage. Although larger numbers of sectors are possible, the number of

20. MOBILE COMMUNICATIONS OVERVIEW 13 FIGURE 2.7: Sectorized antenna system and coverage pattern [20]. antennas and base station equipment become prohibitively expensive for most cell sites [45]. Fig. 2.8 shows a system that employs the 120 type of cell sectorization. In sectoring, capacity is improved while keeping the cell radius unchanged and reducing the D/R ratio. In other words, capacity improvement is achieved by reducing the number of cells and, thus, increasing the frequency reuse. However, in order to accomplish this, it is necessary to reduce the relative interference without decreasing the transmitting power. The co-channel interference in such cellular system is reduced since only two neighboring cells interfere instead FIGURE2.8: Sectorized cellular network employing three sectors, each one covering 120 eld of view.

21. 14 INTRODUCTION TO SMART ANTENNAS (a) (b) FIGURE 2.9: Co-channel interference comparison between (a) omnidirectional and (b) sectorized systems. of six for the omnidirectional case [44, 46] as shown in Fig. 2.9. Increasing the number of sectors in a CDMA system has been a technique useful of increasing the capacity of cell sites [47]. Theoretically, the increase in capacity is proportional to the number of sectors per cell [48]. The penalty for improved signal-to-interference (S/I) ratio and capacity is an increase in the number of antennas at the base station, and a decrease in trunking efciency [13, 46] due to channel sectoring at the base station. Trunking efciency is a measure of the number of users that can be offered service with a particular conguration of xed number of frequencies. 2.6 POWER CONTROL Power control is a technique whereby the transmit power of a base station or handset is decreased close to the lowest allowable level that permits communication [45]. Due to the logarithmic relationship between the capacity of the wireless link and the signal-to-interference-and-noise ratio (SINR) at the receiver [49], any attempt to increase the data rate by simply transmitting more power is extremely costly. Furthermore, increases in power scales up both the desired signals and their mutual interference [28]. Therefore, once a system has become limited by its own interference, power increase is useless. Since mature systems are designed in a way to achieve maximum capacity, it is the power itself, in the form of interference, that ultimately limits their performance [50]. As a result, power must be carefully controlled and allocated to enable the coexistence of multiple geographically dispersed users operating under various

22. MOBILE COMMUNICATIONS OVERVIEW 15 conditions [28] and has been a topic of active research. For example, both GSM and CDMA systems use power control on both uplink and downlink. Particularly, CDMA systems require fast and precise power control since many users share the same RF spectrum, and the system capacity is thus highly sensitive to inadequate interference control [45]. 2.6.1 Spectral Efciency Another effective way to improve the data rate is to increase the signal bandwidth along with power increase. However, the radio spectrum is not an abundant resource in the frequencies of interest. Moreover, increasing the signal bandwidth beyond the coherence bandwidth results in frequency selectivity and degradation in the transmission quality. Spectral efciency, dened as the ratio of capacity per unit bandwidth, measures the ability of a wireless system to deliver information with a given amount of radio spectrum and provides another key metric of the wireless systems quality. It determines the amount of radio spectrum required to provide a given service (e.g., 10 Kbps voice service or 100 Kbps data service) and the number of base stations required to deliver that service to the end-users. In the latter years of deployment, when subscriber penetration is high, it is, consequently, one of the primary determinants of system economics. Spectral efciency is measured in units of bits/second per Hertz/cell (b/s/Hz/cell). It determines the total throughput each base station (cell or sector) can support with a given amount of spectrum. The appearance of a per cell dimension in measuring spectral efciency may seem surprising, but the throughput of a particular base station of a cellular network is almost always substantially less than that of a single cell in isolation. This difference is attributed to the self-interference generated in the network. In a cellular system, the radio communication between a user and a base station gen- erates radio energy that is detectable in places other than the immediate vicinity of the user, the base station and an imaginary line between the two. For other users in the vicinity, this excess energy degrades the radio channel, or makes it completely unusable for conversations. As the user density increases, radio resources are in consequence exhausted eventually. Systems with higher spectral efciency provide more data throughput (services) with a given amount of spectrum and support more users at a given grade of service before experiencing resource ex- haustion. The key benets of higher spectral efciencies can be enumerated as follows: higher aggregate capacity (per-cell throughput); higher per-user quality and service levels; higher subscriber density per base station; small spectrum requirements; and lower capital and opera- tional costs in deployment. The spectral efciency for various systems can be calculated easily using Spectral Efciency = Channel Throughput Channel Bandwidth . (2.1)

23. 16 INTRODUCTION TO SMART ANTENNAS This simply sums the throughput over a channel in an operating network and divides by the channel bandwidth. To understand spectral efciency calculations, consider the PCS-1900 (GSM) system which can be parameterized as follows: 200 KHz carriers, 8 time slots per carrier, 13.3 Kbps of user data per slot, effective reuse of 7 (i.e., effectively 7 channel groups at 100 percent network load, or only 1/7th of each channels throughput available per cell). The spectral efciency is therefore: SE = 8 slots 13.3 Kbps slot /200 KHz/7 cells = 0.076 b/s/Hz/cell. (2.2) This value of approximately 0.1 b/s/Hz/cell is generally representative of high-mobility 2G and 3G cellular systems, including CDMA systems of all types. It reects the fact that the classical techniques for increasing spectral efciency have been exhausted and that new techniques are necessary [45]. Finally, it should be noted that the value of approximately 0.1 b/s/Hz/cell represents a major stumbling block for the delivery of next-generation services. Without substantial increases in spectral efciency, 3G systems are bound to spectral efciencies like those of todays 2G systems. In a typical 3G system with a 5 MHz downlink channel block, this translates into a total cell capacity of approximately 500 Kbps for the entire cell. With services advertised in the range of 144384 Kbps, 13 users will completely occupy the cell capacity! This is far from the approximately 250500 subscribers per cell needed to make the system economically viable, and it underscores the need for new methods to boost spectral efciency. 2.7 MULTIPLE ACCESS SCHEMES Mobile communications utilize the range of available frequencies in a number of ways, referred to as multiple-access schemes. Some basic schemes are FDMA, TDMA, CDMA, and OFDM. 2.7.1 FDMA In the standard analog frequency division multiple access (FDMA) systems, such as AMPS, the most widely cellular phone system installed in North America, different carrier frequencies are allocated to different users. Individual conversations use communication channels appropriately separated in the frequency domain. In a system using the FDMA scheme, six frequencies are assigned to six users, and six simultaneous calls may be made as shown in Fig. 2.10(a). FDMA systems transmit one voice circuit per channel. Each conversation gets its own, unique, radio channel. The channels are relatively narrow, usually 30 KHz or less, and are dened as either transmit or receive channels. A full duplex conversation requires a transmit and receive

24. MOBILE COMMUNICATIONS OVERVIEW 17 Frequency Division Multiple Access Carrier Frequency 1 Carrier Frequency 2 Carrier Frequency 3 Carrier Frequency 4 Carrier Frequency 5 Carrier Frequency 6 (a) TS 1 TS 2 TS 3 Carrier Frequency 1 TS 1 TS 2 TS 3 Carrier Frequency 2 Time Division Multiple Access (b) Code Division Multiple Access Carrier Frequency 1 Code 1 Code 2 Code 3 Code 4 Code 5 Code 6 (c) FIGURE 2.10: Channel usage for different multiple access schemes: (a) FDMA; (b) TDMA; (c) CDMA [40]. channel pair. For example, if a FDMA system had 200 channels, the system can handle 100 simultaneously full duplex conversations (100 channels for transmitting and 100 channels for receiving). 2.7.2 TDMA With time division multiple access (TDMA) systems, separate conversations in both frequency and time domains take place, as shown in Fig. 2.10(b). Each frequency (channel) supports multiple conversations, which use the channel during specic time slots. Typically there is a

25. 18 INTRODUCTION TO SMART ANTENNAS maximum number of conversations which can be supported on each physical channel and each conversation occupies a logical channel. For example, a system using this scheme creates two TDMA channels and divides each into three time slots, serving six users. Global System Mobile (GSM) communications, a unied pan-European system, is a time division-based digital cellular system. It employs 8 user time slots per frame in a 200 KHz channel. Like other TDMA systems, staggered transmit and receive time slots allow modems to use half-duplex radios, thereby reducing their costs. The transmit/receive offset still leaves enough idle time for the mobile to participate in handovers by monitoring neighboring cell channel signal strengths. 2.7.3 CDMA Code Division Multiple Access (CDMA) systems use spread-spectrum (SS) signaling to create wideband sequences for transmission. This is achieved by several methods, such as pseudonoise (PN) sequences, frequency- or time-hopping techniques, as shown in Fig. 2.10(c). A number of users simultaneously and asynchronously access a channel by modulating their information- bearing signals with preassigned signature sequences [51]. In the case of PN sequences, for example, also known as Direct Sequence CDMA (DS-CDMA), each user in the system uses a separate code for transmission, as shown in Fig. 2.10(c). The design aims to spread the bandwidth of the information sequence by mul- tiplying it by a PN sequence yielding a longer random sequence and simultaneously reducing the spectral density of the signal [40]. This new sequence consists of inverted and non- inverted versions of the original PN sequence. Since it is noisy-like, it possesses a wider frequency bandwidth that is less susceptible to the effects of noise and narrowband jammers during transmission [52]. CDMA systems provide protection against multipath interference and antijamming capability. Additionally, there is low probability of interception and, thus, unauthorized parties become less capable of detecting the information message during transmission. In frequency hopping CDMA (FH-CDMA), each user is identied by a unique spreading sequence to create a pseudo random hop pattern of the transmission frequencies over the entire bandwidth. These sequences are available at the receiver to identify the users. In frequency hopping CDMA, the carrier frequency of the modulated information signal is not constant but changes periodically. During time intervals T, the carrier frequency remains the same, but after each time interval the carrier hops to another (or possibly the same) frequency. The hopping pattern is decided by the spreading code. The set of available frequencies the carrier can attain is called the hop-set. The frequency occupation of an FH-SS system differs considerably from a DS-SS system. A DS system occupies the entire frequency band when it transmits, whereas an FH system uses only a small part of the bandwidth when it transmits, but the location of this part differs in time.

26. MOBILE COMMUNICATIONS OVERVIEW 19 In time-hopping CDMA (TH-CDMA), the information-bearing signal is not transmitted continuously. Instead, the signal is transmitted in short bursts at time intervals determined by the spreading code assigned to the user. In-time hopping CDMA the data signal is transmitted in rapid bursts at time intervals. The time axis is divided into frames, and each frame is divided into M, for example, time slots. During each frame the user transmits in one of the M time slots. The code signal assigned to the user denes which of the M time slots is transmitted. Since a user transmits all of its data in one, instead of M time slots, the frequency it needs for its transmission increases by a factor of M. In theory, the capacity provided by the three multiple access schemes is the same and is not altered by dividing the spectrum into frequencies, time slots, or codes, as explained in the following example [53]. Assume that there are six carrier frequencies available for transmission covering the available bandwidth. The channel usage for FDMA, TDMA, and CDMA is depicted in Fig. 2.10. In a system using the FDMA scheme, six frequencies are assigned to six users, and six simultaneous calls may be made. TDMA generally requires a larger bandwidth than FDMA. A system using this scheme can create two TDMA channels and divides each into three time slots, serving six users [40]. A CDMA channel requires a larger bandwidth than the other two and serves six calls by using six codes, as illustrated in Fig. 2.10(c). 2.7.4 OFDM The principle of orthogonal frequency division multiple (OFDM) access has existed for several decades. However, it was only in the last decade that it started to be used in commercial systems. Digital Audio and Video Broadcasting (DAB and DVB), wireless local area networks (WLAN), and more recently wireless local loop (WLL) are the most important wireless applications that use OFDM [54]. The main concept of the method is that one data stream, of Q bps for example, is divided into N data streams, each at a rate of Q/N bps where each one is carried by a different frequency. In OFDM, the subcarrier pulse used for transmission is chosen to be rectangular. This has the advantage that the task of pulse forming and modulation can be performed by a simple Inverse Discrete Fourier Transform (IDFT). Thus, the N data streams are combined together using the Inverse Fast Fourier Transform (IFFT), which can be implemented very efciently, to obtain a time-domain waveform for transmission as an IFFT. Therefore, in the receiver, a forward FFT is needed to reverse this operation. According to the theorems of the Fourier Transform the rectangular pulse shape will lead to a sin(x)/x spectrum of the subcarriers as shown in Fig. 2.11. The parallel, and slower data streams, are allowed to overlap in frequency. In this way, the bandwidth of the modulated symbol effectively decreases by N, and its duration increases by N, as well. Therefore, with the appropriate choice of N, frequency-selectivity and ISI (Inter

27. 20 INTRODUCTION TO SMART ANTENNAS -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Normalized Frequency (f T) -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 NormalizedAmplitude FIGURE 2.11: OFDM and the orthogonality principle. Symbol Interference) can be removed. The carrier frequency spacing f is selected so that each subcarrier is orthogonal to all other subcarriers, thus f = 1/T, where T is the OFDM symbol duration (or, more precisely, the effective duration of the Fourier transform). OFDM is particularly suited for transmission over a dispersive (i.e., frequency selective) channel. In 1993 Linnertz et al. proposed the multi-carrier code division multiple access (MC- CDMA) [55]. It is a new CDMA system based on a combination of CDMA and orthogonal frequency division OFDM where the spreading is performed in the frequency domain, rather than in the time domain as in a DS-CDMA system. In MC-CDMA, each of the M carriers in a multi-carrier system is multiplied by a spreading sequence unique to each user. This system has gained much attention, because the signal can be easily transmitted and received using the Fast Fourier Transform (FFT) device without increasing the transmitter and receiver complexities and is potentially robust to channel frequency selectivity with a good frequency use efciency [56].

28. 21 C H A P T E R 3 Antenna Arrays and Diversity Techniques An antenna in a telecommunications system is the device through which, in the transmission mode, radio frequency (RF) energy is coupled from the transmitter to the free space, and from free space to the receiver in the receiving mode [5759]. 3.1 ANTENNA ARRAYS In many applications, it is necessary to design antennas with very directive characteristics (very high gains) to meet demands for long distance communication. In general, this can only be accomplished by increasing the electrical size of the antenna. Another effective way is to form an assembly of radiating elements in a geometrical and electrical conguration, without necessarily increasing the size of the individual elements [9]. Such a multielement radiation device is dened as an antenna array [59]. The total electromagnetic eld of an array is determined by vector addition of the elds radiated by the individual elements, combined properly in both amplitude and phase[58, 59]. Antenna arrays can be one-, two-, and three-dimensional. By using basic array geometries, the analysis and synthesis of their radiation characteristics can be simplied. In an array of identical elements, there are at least ve individual controls (degrees of freedom) that can be used to shape the overall pattern of the antenna. These are the [59]: i. geometrical conguration of the overall array (linear, circular, rectangular, spherical, etc.) ii. relative displacement between the elements iii. amplitude excitation of the individual elements iv. phase excitation of the individual elements v. relative pattern of the individual elements

29. 22 INTRODUCTION TO SMART ANTENNAS 3.2 ANTENNA CLASSIFICATION In general, antennas of individual elements may be classied as isotropic, omnidirectional and directional according to their radiation characteristics. Antenna arrays may be referred to as phased arrays and adaptive arrays according to their functionality and operation [59]. 3.2.1 Isotropic Radiators An isotropic radiator is one which radiates its energy equally in all directions. Even though such elements are not physically realizable, they are often used as references to compare to them the radiation characteristics of actual antennas. 3.2.2 Omnidirectional Antennas Omnidirectional antennas are radiators having essentially an isotropic pattern in a given plane (the azimuth plane in Fig. 3.1) and directional in an orthogonal plane (the elevation plane in Fig. 3.1). Omnidirectional antennas are adequate for simple RF environments where no specic knowledge of the users directions is either available or needed. However, this unfocused approach scatters signals, reaching desired users with only a small percentage of the overall energy sent out into the environment [4]. Thus, there is a waste of resources using omnidirectional antennas since the vast majority of transmitted signal power radiates in directions other than the desired user. Given this limitation, omnidirectional strategies attempt to overcome environmental challenges by simply increasing the broadcasting power. Also, in a setting of numerous users (and interferers), this makes a bad situation worse in that the signals that miss the intended user become interference for those in the same or adjoining cells. Moreover, the single-element approach cannot selectively reject signals interfering with those of served users. Therefore, it has no spatial multipath mitigation or equalization capabilities. Omnidirectional strategies directly and adversely impact spectral efciency, limiting frequency reuse. These FIGURE 3.1: Omnidirectional antennas and coverage patterns [4].

30. ANTENNA ARRAYS AND DIVERSITY TECHNIQUES 23 limitations of broadcast antenna technology regarding the quality, capacity, and geographic coverage of wireless systems initiated an evolution in the fundamental design and role of the antenna in a wireless system. 3.2.3 Directional Antennas Unlike an omnidirectional antenna, where the power is radiated equally in all directions in the horizontal (azimuth) plane as shown in Fig. 3.1, a directional antenna concentrates the power primarily in certain directions or angular regions [59]. The radiating properties of these antennas are described by a radiation pattern, which is a plot of the radiated energy from the antenna measured at various angles at a constant radial distance from the antenna. In the near eld the relative radiation pattern (shape) varies accorging to the distance from the antenna, whereas in the far eld the relative radiation pattern (shape) is basically independent of distance from the antenna. The direction in which the intensity/gain of these antennas is maximum is referred to as the boresight direction [59, 60]. The gain of directional antennas in the boresight direction is usually much greater than that of isotropic and/or omnidirectional antennas. The radiation pattern of a directional antenna is shown in Fig. 3.2 where the boresight is in the direction = 0 . The plot consists of a main lobe (also referred to as major lobe), which contains the boresight and several minor lobes including side and rear lobes. Between these lobes are directions in which little or no radiation occurs. These are termed minima or nulls. Ideally, the intensity of the eld toward nulls should be zero (minus innite d Bs ). However, practically nulls may represent a 30 or more dB reduction from the power at boresight. The angular segment subtended by two points where the power is one-half the main lobes peak value is known as the half-power beamwidth. 3.2.4 Phased Array Antennas A phased array antenna uses an array of single elements and combines the signal induced on each element to form the array output. The direction where the maximum gain occurs is usually controlled by adjusting properly the amplitude and phase between the different elements [59]. Fig. 3.3 describes the phased array concept. 3.2.5 Adaptive Arrays Adaptive arrays for communication have been widely examined over the last few decades. The main thrust of these efforts has been to develop arrays that would provide both interference protection and reliable signal acquisition and tracking in communication systems [61]. The radiation characteristics of these arrays are adaptively changing according to changes and requirements of the radiation environment. Research on adaptive arrays has involved both theoretical and experimental studies for a variety of applications. The eld of adaptive array

31. 24 INTRODUCTION TO SMART ANTENNAS z x y ( , )0 0P FIGURE 3.2: Radiation pattern of a directional antenna [17]. FIGURE 3.3: Phased array antenna concept [20].

32. ANTENNA ARRAYS AND DIVERSITY TECHNIQUES 25 sensor systems has now become a mature technology, and there is a wealth of literature available on various aspects of such systems [62]. Adaptive arrays provide signicant advantages over conventional arrays in both communication and radar systems. They have well-known advantages for providing exible, rapidly congurable, beamforming and null-steering patterns [62]. However, this is often assumed because of its exibility in using the available array elements in an adaptive mode and, thus, can overcome most, if not all, of the deciencies in the design of the basic or conventional arrays [63]. Therefore, conventional goals, such as low sidelobes and narrow beamwidth in the array design can be ignored in the implementation of an adaptive array. Nevertheless, much work has drawn attention toward these impairments of adaptive arrays and reported the serious problems, such as grating nulls, with improper selection of element distributions and patterns [64]. An adaptive antenna array is the one that continuously adjusts its own pattern by means of feedback control [9]. The principal purpose of an adaptive array sensor system is to enhance the detection and reception of certain desired signals [62]. The pattern of the array can be steered toward a desired direction space by applying phase weighting across the array and can be shaped by amplitude and phase weighting the outputs of the array elements [65]. Additionally, adaptive arrays sense the interference sources from the environment and suppress them automatically, improving the performance of a radar system, for example, without a priori information of the interference location [66]. In comparison with conventional arrays, adaptive arrays are usually more versatile and reliable. A major reason for the progress in adaptive arrays is their ability to automatically respond to an unknown interfering environment by steering nulls and reducing side lobe levels in the direction of the interference, while keeping desired signal beam characteristics [66]. Most arrays are built with xed weights designed to produce a pattern that is a compromise between resolution, gain, and low sidelobes. However, the versatility of the array antenna invites the use of more sophisticated techniques for array weighting [65]. Particularly attractive are adaptive schemes that can sense and respond to a time-varying environment. The precise control of null placement in adaptive arrays results in slight deterioration in the output SNR. Adaptive antenna arrays are commonly equipped with signal processors which can automatically adjust by a simple adaptive technique the variable antenna weights of a signal processor so as to maximize the signal-to-noise ratio. At the receiver output, the desired signal along with interference and noise are received at the same time. The adaptive antenna scans its radiation pattern until it is xed to the optimum direction (toward which the signal-to-noise ratio is maximized). In this direction the maximum of the pattern is ideally toward the desired signal.

33. 26 INTRODUCTION TO SMART ANTENNAS Adaptive arrays based on DSP algorithms can, in principle, receive desired signals from any angle of arrival. However, the output signal-to-interference plus-noise ratio (SINR) obtained from the array, as the desired and interference signal angles of arrival and polarizations vary, depends critically on the element patterns and spacings used in the array [61]. 3.3 DIVERSITY TECHNIQUES Diversity combining [67] is an effective way to overcome the problem of fading in radio channels. It utilizes the fact that if some receive antennas are experiencing a low signal level due to fading, also called a deep fade, some others will probably not suffer from the same deep fade, provided that they are displaced in appropriate positions, or in polarity [68]. Let us now consider the transmission of an information sequence over a frequency non- selective channel. The average bit error probability (BEP) is given by Pb = 0 Pb(b)p(b)db (3.1) where Pb(b) is the bit error probability as a function of the received signal-to-noise-ratio (SNR), b, and p(b) is the probability density function (PDF) of the received SNR. As an example, we examine the transmission of Binary Phase Shift Keying (BPSK) information sequence over a Rayleigh fading channel. In this case, Pb(b) is given by Pb(b) = Q b (3.2) where b = 2 Eb/N0 is the received SNR and Eb is the energy of the transmitted information bit. Moreover, for a Rayleigh fading channel, it can be easily shown that p(b) = 1 b eb / b (3.3) where b is the average SNR dened by b = Eb N0 E 2 (3.4) where E{} denotes the expectation value. Substituting Pb(b) and p(b) into the expression for Pb in (3.1), we obtain the average bit error probability as Pb = 1 2 1 b 1 + b . (3.5) The bit error probabilities for BPSK modulation over AWGN and Rayleigh fading channels are shown in Fig. 3.4. When simulating the performance of any information bearing sequence

34. ANTENNA ARRAYS AND DIVERSITY TECHNIQUES 27 0 5 10 15 20 25 30 b (dB) 10 -5 10 -4 10 -3 10 -2 10 -1 1 Pb Rayleigh fading AWGN channel FIGURE 3.4: Bit error probability for BPSK modulation over AWGN and Rayleigh fading channels. transmitted through a particular wireless channel, a bit error occurs if the decision for the received bit does not match the originally transmitted data bit. The bit error rate (BER) is the ratio of the number of bit errors to the total number of transmitted data bits [69]. From Fig. 3.4, we observe that while the error probability decreases exponentially with SNR for the AWGN channel, it decreases only inversely for the Rayleigh fading channel case [70]. Therefore, fading degrades the performance of a wireless communication system signicantly. In order to combat fading, the receiver is typically provided with multiple replicas of the transmitted signal. In this way, the transmitted information is extracted with the minimum possible number of errors since all the replicas do not typically fade simultaneously. This method is called diversity and is one of the most effective techniques to combat multipath fading. There exist many diversity techniques including temporal, frequency, space, and polarization diversity. A block diagram of a digital communication system with diversity is shown in Fig. 3.5. The diversity combiner combines the received signals from the different diversity branches. The combiner simply exploits the information embedded in each branch to form the decision variable [26, 70]. In temporal diversity, the same signal is transmitted at different times, where the separation between the time intervals is at least equal to the coherence time, Tc . Therefore, the separated in time channels fade independently and thus, proper diversity reception is achieved. Frequency diversity exploits the fact that frequencies separated by at least the coherence bandwidth of the channel, Bc , fade almost independently of each other. Thus, if a signal is

35. 28 INTRODUCTION TO SMART ANTENNAS s(t) + z1(t) Channel 1 Receiver 1 s(t) + z2(t) Channel 2 Receiver 2 s(t) + zL(t) Channel L Receiver L Combiner Decision variable FIGURE 3.5: Model of a digital communication system with diversity [70]. transmitted simultaneously using frequencies appropriately apart from each other, the receiver is provided with independent fading branches through several frequency channels. In spatial (antenna) diversity, spatially separated antennas are used at the transmitter and/or the receiver. In this way, the replicas of the transmitted signal are provided to the receiver via separate spatial channels [26, 70]. It has been shown that a spatial separation of at least half-wavelength is necessary that the signals received from antenna elements are (almost) independent in a rich scattering, or more precisely in a uniform scattering environment [71]. In antenna diversity, signals received by the different antenna branches are demodulated to baseband with quadrature demodulator and processed with correlator or matched lter detector. The output is then applied to a diversity combiner. This procedure guarantees that fading will be slow and generally not change through a time slot. The option to select the best antenna signicantly improves performance [68]. One method of combining in spatial diversity is to weight each diversity branch with its complex conjugate of its own channel gain (so that the phase introduced by the channel to be as much as possible removed). The combiner then adds the outputs of this process from each individual branch to form its decision. This technique is also known as the maximum ratio combiner (MRC) and is the optimal diversity scheme. However, it needs perfect channel knowledge for maximum performance. Although optimal, MRC is expensive to implement and requires an accurate tracking of the complex fading which is difcult to achieve in practice [26]. Equal gain combining (EGC) diversity technique is a simple alternative to MRC. It consists of the co-phasing of the signals received from each diversity branch using unit weights before added by the combiner [26]. The

36. ANTENNA ARRAYS AND DIVERSITY TECHNIQUES 29 performance of EGC is found to be very close to that of MRC. The SNR of the combined signals using EGC is only 1 dB below the SNR provided by MRC [72]. In switched diversity (SC), the decision is made using a branch with SNR larger than a predetermined threshold. If the SNR drops below this threshold, the combiner switches to another branch that satises the threshold criterion. Another combining scheme is selection diversity (SC) in which all the branches are monitored simultaneously [70]. The branch yielding the highest SNR ratio is always selected at any one time. The received signal is then multiplied by the complex conjugate of the corresponding branch. The formed decision is based upon this output. At this point, it would be useful to see the performance of a particular antenna diversity scheme. For example, employing MRC and BPSK modulation, the probability of bit error is given by Pb = 1 2 L L 1 l = 0 L 1 + l l 1 + 2 l (3.6) where L is the number of the present diversity branches and = b 1 + b . For large values of the average bit-to-noise ratio, (3.6) simplies to [73] Pb 1 4 b 2L 1 L . (3.7) Thus, at high values of SNR, it possesses in its diagram a slope approximately equal to L dB/decade. Fig. 3.6 shows the performance of MRC for different number of branches L. As the diversity order increases, the BER performance is improved, or equivalently there is a signicant gain in SNR for a given BER. However, this increase in performance is accompanied by the trade-off of more expensive and complicated infrastructure and additional required transmission power. The polarization diversity scheme achieves its diversity based on the different propagation characteristics of the vertically and horizontally polarized electromagnetic waves [74]. Polarization diversity is different from space diversity. It is based on the concept that in high multipath environments, the signal from a portable received at the base station has varying polarization. The mechanism of decorrelation for the different polarizations is the multipath reections encountered by a signal traveling between the portable and base station. Typically, an improvement in the uplink performance can be achieved by using two receive antennas with orthogonal polarizations and combining these signals. Because the two receive antennas do not need to be spaced apart horizontally to accomplish this, they can be mounted under the same radome [75]. Polarization diversity does have its benets. It is easy to obtain a suitable site

37. 30 INTRODUCTION TO SMART ANTENNAS 0 5 10 15 20 25 30 35 40 b (dB) 10 -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 1 Pb MRC diversity AWGN channel L = 1 L = 2 L = 3L = 4 FIGURE 3.6: The MRC diversity technique [72]. because large structures that are required for space-diversity techniques are not needed. But polarization diversity is completely effective only in high multipath environments. Some manufacturers have promoted polarization diversity as performing better than space diversity in all environments [75]. However, when high multipath environments do not exist, the performance of the polarization-diversity antennas may not be as good as the space-diversity system. Polar- ization diversity is a useful technique in the proper environment, where the necessary multipath is present. Before assuming that polarization diversity may work in a particular environment, eld testing must be performed to compare space diversity and polarization diversity. Angular antenna diversity has been considered as an attempt to control the dispersive type of fading along with the traditional antenna space diversity being utilized to reduce the impact of at fading [76]. In angle diversity, antennas with narrow beamwidths are positioned in different angular directions or regions. The use of narrower beams increases the gain of the base station antenna and provides angular discrimination that can reduce interference [77]. Furthermore, it has been shown practically, by Perini [77] and others, that the effect of angular diversity is quite similar to that of using space diversity, especially in dense urban areas. Fig. 3.7 shows three antenna diversity options with four antenna elements for a 120 sectorized system. Fig. 3.7(a) shows spatial diversity with approximately seven wavelengths (7) spacing between the elements (3.3 m at 1900 MHz). A typical antenna element has a gain of 18 dBi. The horizontal and vertical beamwidths are 65 and 80 , respectively. Fig. 3.7(b) shows two dual polarization antennas, where the antennas can be either closely spaced (/2) to provide both angle and polarization diversity in a small prole, or widely spaced (7) to provide both spatial and polarization diversity [20]. The antenna elements shown are the commonly

38. ANTENNA ARRAYS AND DIVERSITY TECHNIQUES 31 FIGURE3.7: Antenna diversity options with four antenna elements: (a) spatial diversity; (b) polarization diversity with angular and spatial diversity; (c) angular diversity [20]. used 45 slant polarization antennas, rather than vertically and horizontally polarized antennas. Finally, in Fig. 3.7(c) a closely spaced (/2) vertically polarized array is shown. Such an array provides angle diversity in a small prole [20].

39. 33 C H A P T E R 4 Smart Antennas 4.1 INTRODUCTION Many refer to smart antenna systems as smart antennas, but in reality antennas by themselves are not smart. It is the digital signal processing capability, along with the antennas, which make the system smart. Although it may seem that smart antenna systems are a new technology, the fundamental principles upon which they are based are not new. In fact, in the 1970s and 1980s two special issues of the IEEE Transactions on Antennas and Propagation were devoted to adaptive antenna arrays and associated signal processing techniques [78, 79]. The use of adaptive antennas in communication systems initially attracted interest in military applications [27]. Particularly, the techniques have been used for many years in electronic warfare (EWF) as coun- termeasures to electronic jamming. In military radar systems, similar techniques were already used during World War II [80]. However, it is only because of todays advancement in powerful low-cost digital signal processors, general-purpose processors and ASICs (Application Specic Integrated Circuits), as well as innovative software-based signal processing techniques (algorithms), that smart antenna systems are gradually becoming commercially available [17, 59]. 4.2 NEED FOR SMART ANTENNAS Wireless communication systems, as opposed to their wireline counterparts, pose some unique challenges [42]: i. the limited allocated spectrum results in a limit on capacity ii. the radio propagation environment and the mobility of users give rise to signal fading and spreading in time, space and frequency iii. the limited battery life at the mobile device poses power constraints In addition, cellular wireless communication systems have to cope with interference due to frequency reuse. Research efforts investigating effective technologies to mitigate such effects have been going on for the past twenty ve years, as wireless communications are experiencing rapid growth [42]. Among these methods are multiple access schemes, channel coding and

40. 34 INTRODUCTION TO SMART ANTENNAS FIGURE 4.1: Wireless systems impairments [81]. equalization and smart antenna employment. Fig. 4.1 summarizes the wireless communication systems impairments that smart antennas are challenged to combat. An antenna in a telecommunications system is the port through which radio frequency (RF) energy is coupled from the transmitter to the outside world for transmission purposes, and in reverse, to the receiver from the outside world for reception purposes [57, 59]. To date, antennas have been the most neglected of all the components in personal communications systems. Yet, the manner in which radio frequency energy is distributed into and collected from space has a profound inuence upon the efcient use of spectrum, the cost of establishing new personal communications networks and the service quality provided by those networks [20]. The commercial adoption of smart antenna techniques is a great promise to the solution of the aforementioned wireless communications impairments. 4.3 OVERVIEW The basic idea on which smart antenna systems were developed is most often introduced with a simple intuitive example that correlates their operation with that of the human auditory system. A person is able to determine the Direction of Arrival (DoA) of a sound by utilizing a three-stage process:

41. SMART ANTENNAS 35 FIGURE 4.2: Human auditory function [17]. r Ones ears act as acoustic sensors and receive the signal. r Because of the separation between the ears, each ear receives the signal with a different time delay. r The human brain, a specialized signal processor, does a large number of calculations to correlate information and compute the location of the received sound. To better provide an insight of how a smart antenna system works, let us imagine two persons carrying on a conversation inside an isolated room as illustrated in Fig. 4.2. The listener among the two persons is capable of determining the location of the speaker as he moves about the room because the voice of the speaker arrives at each acoustic sensor, the ear, at a different time. The human signal processor, the brain, computes the direction of the speaker from the time differences or delays received by the two ears. Afterward, the brain adds the strength of the signals from each ear so as to focus on the sound of the computed direction. Utilizing a similar process, the human brain is capable of distinguishing between multiple signals that have different directions of arrival. Thus, if additional speakers join the conversation, the brain is able to enhance the received signal from the speaker of interest and tune out unwanted interferers. Therefore, the listener has the ability to distinguish one persons voice, from among many people talking simultaneously, and concentrate on one conversation at a time. In this way, any unwanted interference is attenuated. Conversely, the listener can respond back to the same direction of the desired speaker by orienting his/her transmitter, his/her mouth, toward the speaker. Electrical smart antenna systems work the same way using two antennas instead of two ears, and a digital signal processor instead of the brain as seen in Fig. 4.3. Thus, based on the

42. 36 INTRODUCTION TO SMART ANTENNAS w1 w2 + DSP FIGURE 4.3: A two-element electrical smart antenna. time delays due to the impinging signals onto the antenna elements, the digital signal processor computes the direction-of-arrival (DOA) of the signal-of-interest (SOI), and then it adjusts the excitations (gains and phases of the signals) to produce a radiation pattern that focuses on the SOI while tuning out any interferers or signals-not-of-interest (SNOI). Transferring the same idea to mobile communication systems, the base station plays the role of the listener, and the active cellular telephones simulate the role of the several sounds heard by human ears. The principle of a smart antenna system is illustrated in Fig. 4.4. A digital signal processor located at the base station works in conjunction with the antenna array and is responsible for adjusting various system parameters to lter out any interferers or signals-not-of-interest (SNOI) while enhancing desired communication or signals-of-interest (SOI). Thus, the system forms the radiation pattern in an adaptive manner, responding dynamically to the signal environment and its alterations. The principle of beamforming is essentially to weight the transmit signals in such a way that the receiver obtains a constructive super- position of different signal parts. Note that some knowledge of the transmission channel at the transmitter is necessary in order for beamforming to be feasible [82]. A comprehensive overview of beamforming techniques is given in [83]. Fig. 4.5 illustrates the general idea of adaptive beamforming.

43. SMART ANTENNAS 37 intelligence control RF in/out To/from radio subsystem Steerable lobe Antenna element FIGURE 4.4: Principle of a smart antenna system [80]. 4.4 SMART ANTENNA CONFIGURATIONS Basically, there are two major congurations of smart antennas: r Switched-Beam: A nite number of xed, predened patterns or combining strategies (sectors). r Adaptive Array: A theoretically innite number of patterns (scenario-based) that are adjusted in real time according to the spatial changes of SOIs and SNOIs. In the presence of a low level interference, both types of smart antennas provide signicant gains over the conventional sectorized systems. However, when a high level interference is present, the interference rejection capability of the adaptive systems provides signicantly more coverage than either the conventional or switched beam system [4]. Fig. 4.6 illustrates the relative coverage area for conventional sectorized, switched-beam, and adaptive antenna systems. Both types of smart antenna systems provide signicant gains over conventional sectorized systems. The low level of interference environment on the left represents a new wireless system with lower penetration levels. However the environment with a signicant level of interference on the right represents either a wireless system with more users or one using more aggressive frequency reuse patterns. In this scenario, the interference rejection capability of the adaptive system provides signicantly more coverage than either the conventional or switched beam systems [4].

44. 38 INTRODUCTION TO SMART ANTENNAS (a) SOI SNOISNOI (b) FIGURE 4.5: Adaptation procedure: (a) Calculation of the beamformer weights [20] and (b) Beam- formed antenna amplitude pattern to enhance SOI and suppress SNOIs. Now, let us assume that a signal of interest and two co-channel interferers arrive at the base station of a communications system employing smart antennas. Fig. 4.7 illustrates the beam patterns that each conguration may form to adapt to this scenario. The switched-beam system is shown on the left while the adaptive system is shown on the right. The light lines indicate the signal of interest while the dark lines display the direction of the co-channel interfering signals. Both systems direct the lobe with the greatest intensity in the general direction of the signal of interest. However, switched xed beams achieve coarser pattern

45. SMART ANTENNAS 39 Conventional Sectorization Adaptive Switched Beam Conventional Sectorization Switched Beam Adaptive Low Interference Environment Significant Interference Environment FIGURE 4.6: Coverage patterns for switched beam and adaptive array antennas [20]. Switched strategy Adaptive strategy FIGURE 4.7: Beamforming lobes and nulls that Switched-Beam (left) and Adaptive Array (right) systems might choose for identical user signals (light line) and co-channel interferers (dark lines) [20]. control than adaptive arrays [84]. The adaptive system chooses a more accurate placement, thus providing greater signal enhancement. Similarly, the interfering signals arrive at places of lower intensity outside the main lobe, but again the adaptive system places these signals at the lowest possible gain points. The adaptive array concept ideally ensures that the main signal receives maximum enhancement while the interfering signals receive maximum suppression. 4.4.1 Switched-Beam Antennas A switched-beam system is the simplest smart antenna technique. It forms multiple xed beams with heightened sensitivity in particular directions. Such an antenna system detects signal strength, chooses from one of several predetermined xed beams, and switches from one beam to another as the cellular phone moves throughout the sector, as illustrated in Fig. 4.8.

46. 40 INTRODUCTION TO SMART ANTENNAS FIGURE 4.8: Switched-beam coverage pattern [85]. The switched-beam, which is based on a basic switching function, can select the beam that gives the strongest received signal. By changing the phase differences of the signals used to feed the antenna elements or received from them, the main beam can be driven in different directions throughout space. Instead of shaping the directional antenna pattern, the switched-beam systems combine the outputs of multiple antennas in such a way as to form narrow sectorized (directional) beams with more spatial selectivity that can be achieved with conventional, single-element approaches. Other sources in the literature [86] dene this concept as phased array or multibeam antenna. Such a conguration consists of either a number of xed beams with one beam turned on toward the desired signal or a single beam (formed by phase adjustment only) that is steered toward the desired signal. A more generalized to the Switched-Lobe concept is the Dynamical Phased Array (DPA). In this concept, a direction of arrival (DOA) algorithm is embedded in the system [20]. The DOA is rst estimated and then different parameters in the system are adjusted in accordance with the desired steering angle. In this way the received power is maximized but with the trade-off of more complicated antenna designs. The elements used in these arrays must be connected to the sources and/or receivers by feed networks. One of the most widely-known multiple beamforming networks is the Butler matrix [87, 88]. It is a linear, passive feeding, N N network with beam steering capabilities

47. SMART ANTENNAS 41 1R 2L 2R 1L Fixed phase shifters 3-dB coupler 1 2 3 4 - 4 4 FIGURE 4.9: A schematic diagram of a 4 4 Butler matrix [90]. for phased array antennas with N outputs connected to antenna elements and N inputs or beam ports. The Butler matrix performs a spatial fast Fourier transform and provides N orthogonal beams, where N should be an integer power of 2 (i.e. N = 2n , n Z+ ) [89]. These beams are linear independent combinations of the array element patterns. A Butler matrix-fed array can cover a sector of up to 360 depending on element patterns and spacing. Each beam can be used by a dedicated transmitter and/or receiver and the appropriate beam can be selected using an RF switch. A Butler matrix can also be used to steer the beam of a circular array by exciting the Butler matrix beam ports with amplitude and phase weighted inputs followed by a variable uniform phase taper [89]. The only required transmit/receive chain combines alternate rows of hybrid junctions (or directional couplers) and xed phase shifters [90]. Fig. 4.9 shows a schematic diagram of a 4 4 Butler matrix. A total of (N/2) log2 N hybrids and (N/2) log2(N 1) xed phase shifters are required to form the network. The hybrids can be either 90 or 180 3 dB hybrids, depending on if the beams are to be symmetrical distributed about the broadside or whether one of the beams is to be in the broadside direction [91]. A Butler matrix serves two functions: i. distribution of RF signals to radiating antenna elements and ii. orthogonal beam forming and beam steering. By connecting a Butler matrix between an antenna array and an RF switch, multiple beamforming can be achieved by ex

Introduction to smart antenna

Documents

Transcript of Introduction to smart antenna